The leading policy to conserve forest is protected areas (PAs). Yet, they are not a single tool: land users and uses vary by PA type; and public PA strategies vary in the extent of each type, as well as in the determinants of impact for each type, i.e. siting and internal deforestation. Further, across regions and time, strategies respond to pressures (deforestation and political).We estimate deforestation impacts of PA types for a critical frontier, the Brazilian Amazon. We separate regions and time periods that differ in their deforestation and political pressures and document considerable variation in PA strategies across regions, time periods and types. The siting of PAs varies across regions. For example, all else being equal, PAs in the arc of deforestation are relatively far from non-forest, while in other states they are relatively near. Internal deforestation varies across time periods, e.g. it is more similar across the PAtypes for PAs after 2000. By contrast, after 2000, PA extent is less similar across PA types with little non-indigenous area created inside the arc. PA strategies generate a range of impacts for PA types—always far higher within the arc—but not a consistent ranking of PA types by impact.

Quasi-experimental methods increasingly are used to evaluate the impacts of conservation interventions by generating credible estimates of counterfactual baselines. These methods generally require large samples for statistical comparisons, presenting a challenge for evaluating innovative policies implemented within a few pioneering jurisdictions. Single jurisdictions often are studied using comparative methods, which rely on analysts’ selection of best case comparisons. The synthetic control method (SCM) offers one systematic and transparent way to select cases for comparison, from a sizeable pool, by focusing upon similarity in outcomes before the intervention. We explain SCM, then apply it to one local initiative to limit deforestation in the Brazilian Amazon. The municipality of Paragominas launched a multi-pronged local initiative in 2008 to maintain low deforestation while restoring economic production. This was a response to having been placed, due to high deforestation, on a federal “blacklist” that increased enforcement of forest regulations and restricted access to credit and output markets. The local initiative included mapping and monitoring of rural land plus promotion of economic alternatives compatible with low deforestation. The key motivation for the program may have been to reduce the costs of blacklisting. However its stated purpose was to limit deforestation, and thus we apply SCM to estimate what deforestation would have been in a (counterfactual) scenario of no local initiative. We obtain a plausible estimate, in that deforestation patterns before the intervention were similar in Paragominas and the synthetic control, which suggests that after several years, the initiative did lower deforestation (significantly below the synthetic control in 2012). This demonstrates that SCM can yield helpful land-use counterfactuals for single units, with opportunities to integrate local and expert knowledge and to test innovations and permutations on policies that are implemented in just a few locations.

Protected areas are the leading forest conservation policy for species and ecoservices goals and they may feature in climate policy if countries with tropical forest rely on familiar tools. For Brazil’s Legal Amazon, we estimate the average impact of protection upon deforestation and show how protected areas’ forest impacts vary significantly with development pressure.We use matching, i.e., comparisons that are apples-to-apples in observed land characteristics, to address the fact that protected areas (PAs) tend to be located on lands facing less pressure. Correcting for that location bias lowers our estimates of PAs’ forest impacts by roughly half. Further, it reveals significant variation in PA impacts along development-related dimensions: for example, the PAs that are closer to roads and the PAs closer to cities have higher impact. Planners have multiple conservation and development goals, and are constrained by cost, yet still conservation planning should reflect what our results imply about future impacts of PAs.

Although developing countries have established scores of new protected areas over the past three decades, they often amount to little more than ‘‘paper parks’’ that are chronically short of the financial, human, and technical resources needed for effective management. It is not clear whether and how severely under-resourced parks affect deforestation. In principle, they could either stem it by, for example, creating an expectation of future enforcement, or they could spur it by, for example, creating open access regimes. We examine the effect of Mexico’s natural protected areas (NPAs) on deforestation from 1993 to 2000, a period when forest clearing was rampant and the vast majority of protected areas had negligible resources or management. We use high-resolution satellite data to measure deforestation and (covariate and propensity score) matching to control for NPAs’ nonrandom siting and for spillovers. Our broad finding is that Mexico’s paper parks had heterogeneous effects both inside and outside their borders. More specifically, at the national-level, we cannot reject the null hypothesis that NPAs had zero average effect on clearing inside their borders, nor can we reject a similar hypothesis for spillover clearing outside their borders. However, we can detect statistically and economically significant inside- and outside-NPA effects for certain geographic regions. Moreover, these effects have different signs depending on the region. Finally, we find that NPAs with certain characteristics were more effective at stemming deforestation inside their borders, namely, those that were large, new, mixed use, and relatively well-funded. Taken together, these results suggest that paper parks have the potential to either reduce or exacerbate tropical deforestation and highlight the need for further research on the conditions that lead to each outcome.

New infrastructure is needed globally to support economic development and improve human well-being. Investments that do not consider ecosystem services (ES) can eliminate these important societal benefits from nature, undermining the development benefits infrastructure is intended to provide. Such tradeoffs are acknowledged conceptually but in practice have rarely been considered in infrastructure planning. Taking road investments as one important case, here we examine where and what forms of ES information have the potential to meaningfully influence decisions by multilateral development banks (MDBs). Across the stages of a typical road development process, we identify where and how ES information could be integrated, likely barriers to the use of available ES information, and key opportunities to shift incentives and thereby practice. We believe inclusion of ES information is likely to provide the greatest development benefit in early stages of infrastructure decisions. Those strategic planning stages are typically guided by in-country processes, with MDBs playing a supporting role, making it critical to express the ES consequences of infrastructure development using metrics relevant to government decision makers. This approach requires additional evidence of the in-country benefits of cross-sector strategic planning and more tools to lower barriers to quantifying these benefits and facilitating ES inclusion.

We estimate the effects on deforestation that have resulted from policy interactions between parks and payments and between park buffers and payments in Costa Rica between 2000 and 2005. We show that the characteristics of the areas where protected and unprotected lands are located differ significantly. Additionally, we find that land characteristics of each of the policies and of the places where they interact also differ significantly. To adequately estimate the effects of the policies and their interactions, we use matching methods. Matching is implemented not only to define adequate control groups, as in previous research, but also to define those groups of locations under the influence of policies that are comparable to each other. We find that it is more effective to locate parks and payments away from each other, rather than in the same location or near each other. The high levels of enforcement inside both parks and lands with payments, and the presence of conservation spillovers that reduce deforestation near parks, significantly reduce the potential impact of combining these two policies.

How one treats others is important within collective action. We ask if resource scarcity in the past, due to its effects upon past behaviors, influences current other-regarding behaviors. Contrasting theories and empirical findings on scarcity motivate our framed field experiment. Participants are rural Colombian farmers who have experienced scarcity of water within irrigation. We randomly assign participants to groups and places on group canals. Places order extraction decisions. Our treatments are sequences of scarcities: ‘from lower to higher resources’ involves four rounds each of 20, 60, then 100 units of water; ‘from higher to lower resources’ reverses the ordering. We find that upstream farmers extract more, but a lower share, when facing higher resources. Further they take a larger share of higher resources when they faced lower resources in earlier rounds (relative to when facing higher resources initially). That is inconsistent with leading models of responses to scarcity which focus upon one’s own gain. It is consistent with lowering one’s weight on others to, for instance, rationalize having left them little. Our results suggest that facing higher scarcity can erode the bases for collective actions. For establishing new institutions, timing relative to scarcity could affect the probability of success.